Allocating organs through algorithms and equitable access to transplantation—a European human rights law approach

Abstract Digitization in transplantation is not a new phenomenon. Algorithms are being used, for example, to allocate organs based on medical compatibility and priority criteria. However, digitization is accelerating as computer scientists and physicians increasingly develop and use machine learning (ML) models to obtain better predictions on the chances of a successful transplant. The objective of the article is to shed light on the potential threats to equitable access to organs allocated through algorithms, whether these are the consequence of political choices made upstream of digitization or of the algorithmic design, or are produced by self-learning algorithms. The article shows that achieving equitable access requires an overall vision of the algorithmic development process and that European legal norms only partially contribute to preventing harm and addressing equality in access to organs.


I. INTRODUCTION
In 2020, almost 60,000 patients were active on waiting lists for organ transplantation in the European Union (EU). 1 Most organs are transplanted from deceased donors, but several European countries have developed living kidney and (part of) liver donation between siblings or persons with strong emotional ties. While so-called 'altruistic' donations between unrelated living donors and recipients are rarer, crossdonations programs between several donor/recipient pairs are contributing to reduce the gap, although far insufficiently, between need and supply of organs. Organ procurement, 2 including the modalities of consent or authorization, and organ allocation are a matter of state law, but collaborative networks in Central Europe (Eurotransplant), Northern Europe (Scandiatransplant), and Southern Europe (South-Europe Alliance for Transplantation) enable transplantation centers to exchange organs between their respective Member States. 3 These networks cannot solve the shortage on their own, but they do prevent the waste of organs. The EU has facilitated exchanges between EU Member States through the adoption of a directive in 2010, harmonizing the norms of quality and security of cross-border organ exchanges. 4 For the purposes of transparency and safety, the directive imposes the collection of a minimum data set for each transplant. 5 The directive imposes the same standards of protection when EU Member States transfer or receive organs from third countries (including through the above-mentioned networks). 6 In order to manage this scarce resource at the national or international level, countries and above-mentioned networks use algorithms that help to find in a short time the best matches donor-recipient relying on criteria of medical compatibility and priority. Moreover, computer scientists and physicians are increasingly using machine-learning (ML) models for clinical transplantation, whether these models are supervised (when outcomes are defined) or unsupervised (when outcomes are not defined, the model will identify similar patterns in the dataset itself). 7 Researchers develop and use ML to predict the likelihood to need a transplant within a given time; patient's survival on the waiting list; graft survival or rejection after transplantation, including by learning from biopsy pictures, etc. Deep learning (DL) algorithms enable researchers to rely on sophisticated models of interactions between the characteristics of the organ and the recipients to provide a more suitable solution for patients waiting for transplants. 8 The use of such trained algorithms could enable medical doctors to avoid ruling out a potential kidney transplant because of usual risks when the algorithm suggests that, in a patient's particular situation, the transplant is likely to be successful. To carry out their studies, computer scientists use different types of algorithms, sometimes concomitantly, including artificial neural networks or random forests that are better at capturing the synergy between variables. The development of algorithms and in particular ML in clinical transplantation is promising for patients on the waiting list, increasing their chances to get a transplant, and avoiding the loss of donated organs.
Nonetheless, despite their incommensurate benefits, algorithms often lack of transparency and may produce unfair outputs that are difficult to detect. Therefore, the increasing development and use of algorithms in transplantation calls for scrutiny of how algorithms are built and trained in order to achieve their goals and ensure equitable access to transplants.
Non-discrimination and equitable access to health resources are guaranteed by several European legal instruments and programs, at the level of both the Council of Europe and the EU. While the Council of Europe is the main international organization on Human Rights in Europe and composed of 46 States, the EU is a supranational political and economic union of 27 States. 9 Although these two organizations have different foundations, they interact in multiple ways. In particular, there is a presumption of equivalent human rights protection. 10 In November 2019, the committee on Bioethics of the Council of Europe adopted a strategic action plan on Human Rights and Technologies in biomedicine to promote, in particular, equity in healthcare. 11 The action plan encourages Member States to 'ensure that innovative treatments and technologies are available "in an equitable and timely manner"' and to combat health disparities. 12 With regard to the use of digital systems, studies have demonstrated the risks that the development of algorithms (especially ML or DL) could pose to fundamental 7 For an overview of the use of algorithms in the field see Katie L. Connor  rights, including biases leading to a breach of equality between patients. 13 These concerns had led to the adoption of several guidelines and have been partly included in regulatory projects, especially the recent proposal for a European Regulation on Artificial Intelligence (Proposal of AI Act). 14 However, it is important to stress that in most cases, algorithms extend risks rather than create them. Indeed, the risks exclusively related to the learning process when using self-learning algorithms are only a fraction of the threats to equitable access to transplantation. For this reason, the present article proposes to analyze their use and development in the field of organ transplantation in their socio-political context rather than focusing on the sole technical aspects.
The objective of the article is to shed light on the potential threats to equitable access to organs allocated through algorithms, whether these are the consequence of political choices made upstream of digitization or of the algorithmic design, or are produced by self-learning algorithms. In that respect, the primarily legal article will include broader issues of distributive justice or bioethics related to transplantation that pre-exist digital technology, insofar as, when transposed to algorithms, they may affect equitable access to organs such as framed by European human rights law. Besides, the article does not only focus on ML algorithms but also includes organ allocation algorithms that have been used for a longer time. Although inspired by research and practices in the United States, the analysis is based on European law. It will show that reaching an equitable access requires to have an overall vision of the algorithmic development process and that European legal norms only contribute partially to prevent harm and to address equality in access to organs.
Section II aims to provide a framework for analysis by providing elements of definition of 'equitable access to organs' in Europe while outlining the related state obligations. In order to do so, the section proceeds to a cross-reading of relevant European legal sources enriched by a health and human rights approach. On this basis, Section III explores how threats to equitable access may happen in the algorithmic context, and discusses, where appropriate, the gap between current legal standards and algorithmic reality. Such threats arise from the data itself or the ways to 13  pre-process it or from the algorithmic development and deployment. At this point, subsections develop on the choices and/or constraints weighing on computer scientists that need to incorporate certain criteria to define what makes a good transplant candidate. Following this examination of the algorithmic architecture, the issue of correlations made by ML algorithms is then considered. This investigation results in conclusive remarks in Section IV on the interests of this human rights approach of the algorithmic allocation of organs and on the remaining challenges to achieve equitable access in Europe. It then suggests to delineate the need for upstream structural reforms and algorithmic due diligence. Some key state obligations are identified.

II. A REALISTIC EUROPEAN LEGAL FRAMING OF EQUITABLE ACCESS TO ORGANS
Allocation of health resources and priority-setting is first a matter of state law. European human rights law provides certain realistic standards concerning the access to scarce biomedical resources (A) and protects the right to be free from discrimination, whatever the forms such discrimination may take. The interpretations are not, however, completely uniform between European bodies (B). In addition to the individual rights approach, a health and human rights approach allows to enrich the definition of equitable access in the particular context of organ transplantation (C).

II.A. Access to Limited Biomedical Resources
In the field of biomedicine and human rights, most of the relevant legal instruments are from the Council of Europe. The Oviedo Convention on Human Rights and Biomedicine and its Additional Protocol on Transplantation of Organs and Tissues guarantee an equitable access to health care. According to the explanatory report of the Convention, '[i]n this context', equitable 'means first and foremost the absence of unjustified discrimination. Although not synonymous with absolute equality, equitable access implies effectively obtaining a satisfactory degree of care'. 15 The Additional Protocol further endorses and requires allocation depending on waiting lists, and based on 'transparent, objective and duly justified rules according to medical criteria'. 16 The explanatory report of the Additional Protocol states that allocation should first be determined by 'the fundamental principles of medical practice [that] apply in all countries'. 17 In addition to these treaties specifically related to biomedicine and human rights, the European social Charter recognizes more broadly the right to protection of health. 18  which guarantees the 'right of everyone to the enjoyment of the highest attainable standard of physical and mental health'. 19 The UN Committee on Economic, social, and Cultural rights (CESCR) provided a comprehensive general comment in which it establishes accessibility for all without discrimination as an inherent component of the right to health. It means that 'health facilities, goods and services must be accessible to all, especially the most vulnerable or marginalized sections of the population, in law and in fact, without discrimination on any of the prohibited grounds'. 20 They also need to be 'acceptable', ie 'respectful of medical ethics and culturally appropriate, ie respectful of the culture of individuals, minorities, peoples and communities, sensitive to gender and life-cycle requirements'. Together with non-discrimination and acceptability, physical accessibility, affordability, and availability of resources are 'essential elements' of the right to health 21 that should be assessed in order to achieve equitable access to health goods and services. With regard to state obligations in relation to health, it is important to note that these instruments also acknowledge the lack of resources and admit that States have some margin in the allocation of resources. This is in line with the case law of the European Court of Human Rights (ECtHR) on health issues. The ECtHR, located in Strasbourg, controls the respect of the European Convention on human rights and fundamental freedoms (ECHR) by the Member States of the Council of Europe. Although the ECHR does not include a right to health as such, the ECtHR has protected health as part as the quality of life under article 8 of the Convention. In Pentiacova and others v. Moldova, the Court found inadmissible the applicants' claim that the insufficient state funding of haemodialysis and the failure to cover their travel costs amounted to a violation of Article 8. The Court considered that the state margin of appreciation to assess priorities was higher when it came to allocating scarce resources. 22 This jurisprudence is consistent with the progressive realization of the right to health, 23 due to difference in resources between countries. Organ transplantation, including from living donors, requires extensive medical expertise and investment in research, and European countries have not developed these programs in the same way and to the same extent. The differences are also related to various cultural and ethical perceptions on organ retrieval on a living or deceased person. 24 Indeed, organs are not simply a limited health resource, they are a biological resource the availability of which requires prior authorization to access a donor's body. The first objective of the above-mentioned treaties of the Council of Europe is to protect the rights and dignity of potential living or deceased donors in the field of transplantation. Living donations for instance are often subsidiary to deceased donations and require approval of an independent body. 25 Although a dead person has no legal personality, some of her rights extend after death and European human rights law protects the human body after death. 26 Organs are therefore not 'available', which reinforces the states' margin of appreciation in managing this resource.
Nevertheless, States must take reasonable steps 27 to resolve the resource allocation dilemma. Progressive realization does not mean elusive obligations, and the CESCR made it clear that some obligations 'such as the guarantee that the right will be exercised without discrimination of any kind (art. 2.2) and the obligation to take steps (art. 2.1) towards the full realization of article 12' are of immediate effect. 28 In its general comment, the CESCR listed among the core obligations 'to ensure the right of access to health facilities, goods and services on a non-discriminatory basis, especially for vulnerable or marginalized groups' but also '[t]o ensure equitable distribution of all health facilities, goods and services'. 29

II.B. The Absence of Discrimination
Apart from being an essential component of the right to health, non-discrimination is a freedom that European law protects either in conjunction with other specific rights, or as such. This section introduces the relevant European instruments, before comparing the prohibited grounds for discrimination, the forms, and potential justification of differential treatments based on protected characteristics. These elements will be relevant for the later analysis of the risks in the algorithmic context.
Although EU primary law addressed equality and non-discrimination in several treaties, the treaty of Lisbon, which entered into force in 2009, greatly contributed to the promotion of equality in the EU by giving a binding nature to the 2000 EU Charter of Fundamental Rights. 30 The EU Charter guarantees equality before the law 25 ), non-discrimination (article 21), and equality between women and men (article 23). The Charter applies to EU institutions, office and agencies, and only to EU Member States when they implement EU law, such as the future European Regulation on AI (AI Act Proposal) concerning high-risks AI systems. 31 Secondary law instruments give expression to the above-mentioned primary law provisions. 32 Among those, several are directly and indirectly relevant to the issue of equitable access to organ transplants. Directive 79/7/EEC and Directive 2004/113/EC prohibit sex discrimination in, respectively, statutory social security schemes and access to goods and services, which applies in the field of health according to the Court of Justice of the EU (CJEU, located in Luxembourg). 33 The Racial Equality Directive applies in relation to social protection including healthcare. 34 At the Council of Europe level, Article 14 ECHR prohibits discrimination in conjunction with other convention rights, such the right to private life, which-as previously stated-has incorporated the protection of health. The adoption of Protocol 12 to the ECHR in 2000 integrated a general freedom not to be discriminated against within the Council of Europe system. The Protocol entered into force in 2005 and is applicable in 20 countries to that date. The European collaborative algorithms the next parts discuss are often used in non-contracting States of the Protocol. 35 These countries remain bound by the ECHR, which guarantees the right to life and the right to private life, both requiring non-discrimination. The catalogue of-essentially-civil and political rights of the ECHR is completed by the European Social Charter, which contains the obligation to treat nationals of other contracting parties on an equal footing with own citizens concerning the right to social and medical assistance (article 13(4)) and a general prohibition of discrimination: article E. 36 Article 14 ECHR and Article 1(1) Protocol 12 prohibit discrimination based on a non-limitative list of protected characteristics: ' . . . any ground such as sex, race, colour, language, religion, political or other opinion, national or social origin, association with a national minority, property, birth or other status.' The ECtHR's jurisprudence interpreted 'other status' as including disability, medical conditions or genetic features, 37 gender identity, sexual orientation, immigration status, and age to some extent. 38 'To some extent' because the Court's scrutiny is not uniform for all protected grounds. Differential treatments based on age are not as strictly scrutinized as discrimination based on sex, sexual orientation, or race/ethnicity. 39 The list of non-limitative protected characteristics in the European Social Charter is mostly similar to that of the ECHR but contains explicitly health as a protected ground. On the EU side, the EU Charter's list of protected characteristics is non-limitative but broader than the ECHR 'on any ground such as sex, race, colour, ethnic or social origin, genetic features, language, religion or belief, political or any other opinion, membership of a national minority, property, birth, disability, age or sexual orientation'. 40 Concerning the forms of discrimination, the CJEU, the ECtHR, and the European Committee on social Rights, which monitors compliance with the European Social Charter in the Council of Europe, 41 all recognize that discrimination may take multiple forms and all prohibit direct and indirect discrimination. Direct discrimination consists in a differential treatment based on a protected characteristic. Indirect discrimination occurs when apparently neutral provisions or measures have a prejudicial effect in practice against a specific protected group. 42 Indirect discrimination and, at least in part, direct discrimination do not require the intent to discriminate in European law. 43 These two forms of discrimination have the potential to cover developers' own biases but also the use of biased data, even when developers did not intend to discriminate but designed a model producing a disadvantage to a protected group. In addition, both European Courts recognized the notion of discrimination by association, to persons 'although not themselves a member of the [protected] group concerned, nevertheless suffer less favourable treatment or a particular disadvantage on one of those grounds' 44 because of a discriminatory treatment against a person they are usually closely connected too. 45 Despite this common base, the European Courts' jurisprudence diverge in some regards to the point that some authors talk about 'invalidating jurisprudential diversity'. 46 One significant difference is on 'intersectional' forms of discrimination. EU law recognizes the existence of multiple discrimination but not 'intersectional' discrimination. 47 In contrast with multiple discrimination that can be additive and cumulative, intersectional discrimination cannot be captured on one single protected characteristic but rather emerges from the interplay between different grounds. 48 By centralizing these various forms of oppression, intersectionality theory offers a reading of non-discrimination law in light of 'a new philosophy of inequality'. 49 To this date, the European Courts did not formally endorse such broadening of the scope of nondiscrimination law. However, while the CJEU has a mechanical interpretation, 50 the ECtHR has a pragmatic and dynamic approach. Although the ECtHR does not directly consider intersectional discrimination, it analyses concomitant forms of discrimination or disparities in connection to a broad and casuistic concept of vulnerability. 51 Beyond the advantages and weaknesses of intersectionality theory, 52 the concept of intersectional discrimination has great potential for addressing the risks associated , in which the domestic authorities failed to take into account the needs of a child with disabilities when they determined his father's eligibility for tax relief on the purchase of adapted property. The Court found that the applicant had been subject to discrimination on the ground of disability on the account of his child disability. 46 Manuela Brillat, Le principe de non-discrimination à l'épreuve des rapports entre les droits européens 123 (2015). 47 'Declaring that no new category of discrimination resulting from the combination of more than one of those grounds [ . . . ] may be found to exist where discrimination on the basis of those grounds taken in isolation has not been stablished'. with ML algorithms, which rely on complex forms of interactions between medical and social characteristics of patients. The vulnerability approach does not provide clear standards to prevent discrimination in algorithmic systems. The proof of non-discrimination is based on a test of comparability of situations, whether direct or indirect discrimination is at stake. If the situations are too different to be comparable, non-discrimination law is not applicable. The analysis of analogy between situations is factual and the interpreter has a broad margin. 53 The way to formulate the respective situations and identify the reference circle for comparison determines their degree of similarity, which consequently affects the scrutiny of the legitimacy and necessity of the challenged treatment. Qualification thus carries political weight. 54 When situations are comparable, ie identical or slightly identical (and there is a differential treatment, with the risk of direct discrimination) or very different (and there is a similar treatment, leading to a potential indirect discrimination), the ECtHR and the European Committee of Social Rights will analyze the justification of this treatment. This contrasts with EU law on direct discrimination, which usually excludes the justification in the context of direct discrimination. 55 In this regard, an applicant claiming to be victim of direct discrimination on the basis of ethnicity would have better chances of success in Luxembourg (CJEU) then in Strasbourg (ECtHR). Nevertheless, EU law does not have a uniform approach and the directive related to equal treatment between men and women in the access to and supply of goods and services, allows for some justification also in case of direct discrimination, 56 stating that the 'directive shall not preclude differences in treatment, if the provision of the goods and services exclusively or primarily to members of one sex is justified [ . . . ]'. Whether required by indirect discrimination only or both direct and indirect discrimination, such justifica-52 Kathy Davis, Intersectionality as buzzword-A sociology of science perspective on what makes a feminist theory successful, 9(1) Feminist theory 67 (2008). 53 Ludovic Hennebel and Hélène Tigroudja, Traité de droit international des droits de l'homme 764 (2018). 54 Concerning the access to goods and services in healthcare, the preamble of EU Directive 2004/113/EC specifies for instance that 'differences between men and women in the provision of healthcare services, which result from the physical differences between men and women, do not relate to comparable situations and therefore, do not constitute discrimination tion consists in the presence of a 'legitimate aim' and 'proportionality between that aim and the means employed'. 57 Where the CJEU, the ECtHR, or the European Committee of Social Rights have found that situations were different yet still comparable, they have identified positive state obligations to address these differences in their interpretation of nondiscrimination. 58 In the wording of the Court of Strasbourg, States need to treat differently persons or groups whose situations are 'significantly different' unless they can provide an objective and reasonable justification for their failure to do so. 59 In the case of persons with disabilities, such measures to ensure substantive equality and combat discrimination constitute a state obligation of 'reasonable accommodation'. 60 Nevertheless, European approaches are not homogeneous either with regard to the basis and the extent of these positive obligations to address the particular circumstances of certain individuals or groups. The European Committee of Social Rights has a more assertive approach than the ECtHR. Previously associated with indirect discrimination, the failure to take positive action has gained autonomy in the Committee's decisions and has become a form of discrimination in itself. 61 Faced with a difference of situation in the exercise of social rights, the European Committee has imposed a 'reinforced obligation of means' 62 on States to make 'measurable progress and to an extent consistent with the maximum use of available resources'. 63 In contrast, the jurisprudence on non-discrimination reveals that the ECtHR is more hesitant to require a general obligation to take substantial positive measures to redress factual differences. 64 (2000)).
However, the interplay of positive measures with the margin of appreciation is variable (when these rights are economic and social in nature, this margin may complicate the identification of a positive obligation). Besides, in practice, the Court recurrently avoids the examination of non-discrimination claims (excluding de facto an analysis of a potential positive obligation deriving from the prohibition of discrimination) when it already found a violation of another article of the ECHR. See for instance the jurisprudence on Roma communities, the Court addresses these inequalities as part of its assessment of article 8 (right to private obligations when people or groups' circumstances were 'relevantly and significantly different', 65 providing that relevance is measured in relation to what is at stake, whereas a certain threshold is required in order for the Court to find that the difference in circumstances is significant. For this threshold to be reached, a measure must produce a particularly prejudicial impact on certain persons as a result of a protected ground, attaching to their situation and in light of the ground of discrimination invoked. 66 When States take specific measures favoring certain groups in order to correct and compensate a pre-existing disadvantage (ex: quotas), the CJEU insisted on the need of proportionate 'special measures', which means they cannot introduce an automatic and unconditional priority. 67 To summarize, European law offers protection against multiple forms of discrimination on non-limitative grounds, but the extent of scrutiny varies depending on the protected ground. While it does not formally recognize 'intersectional' discrimination, the vulnerability approach of the ECtHR enables it to capture multiple and concomitant forms of discrimination, although not systematically. A risk of broadening the scope of non-discrimination would be to inflate the notion to the point where it loses its function of protecting groups particularly at risk. However, neither the EU nor the ECtHR's approaches currently provide clear operating standards to address the risk of discrimination based on complex forms of interactions, which is one of the higher risks of ML. Uncertainties about the extent of positive obligations to redress inequalities within a non-discrimination framework may be offset by an additional health and human rights-based approach to organ allocation. and family life) and is usually reluctant to examine claims of unequal access or unequal treatment as such (as part of article 14), see in particular Hudorovič and others v. Slovenia, App. Nos. 24816/14 and 25140/14 (2020) (finding that article 8 may impose States to take measures to provide access to safe drinking water and sanitation, but refusing to examine the non-discrimination aspect in spite of acknowledging the vulnerability of Roma communities in this regard). On the avoidance strategy on bioethical issues (reproductive claims), see also Evans v. United Kingdom, App. No 6339/05 (2007), at 93-96 (concerning the woman applicant's right to become a genetic parent (v. the right of her former partner not to become a parent), in which the Court found that there was no separate question based on the combination of articles 8 and 14), and S.H. and others v. Austria, No. 57813/03 (2011) at 120, (concerning two couples' access to IVF) in which the Grand Chamber did not find necessary to proceed to the analysis of non-discrimination claims. 65 J.D. and A. European law on biomedicine and human rights is pragmatic when it comes to delineating the scope of a right to access human organs for transplantation, being primarily understood as the absence of discrimination. Human rights law does not provide a uniform definition of equitable access, being rather concerned about the 'fair deliberative process' when it comes to setting priorities in health. 68 Nonetheless, this framing of equitable access can be further refined by a 'health and human rights approach' to access to transplantation. 69 Such a reading allows for the consideration of relevant socio-determinants of health where non-discrimination law relies on protected characteristics to be applicable and does not (at least yet in the European context) fully capture intersecting causes of vulnerability due to the Courts' single-axis approach. Instead of the binary approach of human rights subjects (individual/vulnerable groups broadly defined), social determinants of health interestingly complete this framing by addressing social disparities more gradually: 'to reduce the steepness of the social gradient in health, actions must be universal, but with a scale and intensity that is proportional to the level of disadvantage'. 70 Socio-determinants of health include social and cultural factors, physical environment, income, access to education and health and social services, and abilities to be involved and participate in societal debates. Merging the respective approaches, the UN CESCR incorporated the social determinants of health in its interpretation of the right to health. 71  Socio-demographic factors also play on the acceptance and functioning of transplant. 73 Investigating socio-determinants of health can help to better understand geographic disparities in access to transplantation. At the transnational level, the Eurotransplant algorithm ensures a 'genuine distribution across the participating countries'. 74 Disparities also exist at the intrastate level. They may result from differences in the quantity and quality of transplantation facilities and services between rural and urban areas. In areas where there are few transplant centers, this may also be a barrier to living organ donation and thus to access to transplantation. When geographic distance from a transplant center primarily affects ethnic minorities, accessibility to transplantation for people belonging to these minorities is affected. 75 Yet, little work has been done to identify and mitigate these disparities and improved research in health equity in transplantation is needed. 76 Such studies provide the tools to assess the overall situation of patients and are thus necessary to any comprehensive study on equality in the algorithmic allocation of human organs for transplantation. They enable developers to make informed decisions on whether to include certain disparity factors in mathematical models of organ allocation by assigning them respective weights; or whether to exclude other factors from the models, leaving public authorities to address structural issues upstream and independently of the design of an allocation model. Weighing may be considered as positive action, i.e. a way to treat differently patients found to be in a different situation, meeting some of the above-mentioned requirements of non-discrimination law. This overview of relevant European legal standards relevant for achieving equitable access to scarce biomedical resources, enriched by a health and human rights approach, provides a compass for the development of acceptable digital solutions. 77 Due to the sensitive nature of organ transplantation, the shortage of organs, and the political aspects of non-discrimination law, States usually have a wide margin of appreciation in the allocation of resources. Therefore, achieving equitable access within a European human rights framework will require to assess the situation of respective patients with due regard to their individual rights, a balance of interests, objective justification of allocation, and proportionality. Based on the above, the following section discusses some of the threats to equitable access during the algorithmic development.

III. THE THREATS TO EQUITABLE ACCESS TO TRANSPLANTATION IN ALGORITHMIC DEVELOPMENT
Threats to equitable access can arise at different steps of the overall algorithmic development. This section analyzes the interactions between practices during the algorithmic development and the legal requirements above-mentioned. It first considers potential risks arising from the data feeding self-learning algorithms (A), second, those related to the design choices and constraints during the development of the algorithm (whether ML or not) (B), and third, the threats coming from the learning process when ML is used (C). Certain practices, inactions or processes can undermine the principle of equity and even constitute direct or indirect discrimination in access to transplantation. The section also shows that these current legal standards are not always fully adequate to deal with these respective algorithmic risks.

III.A. Data Input and the Breach of Equality between Patients in Predictive Models
The following sub-sections focus on the data input to ML algorithms and examine how potential threats may emerge from two successive steps preceding algorithm development: data collection (1) and data pre-processing (2), and briefly conclude on the capacity of proposed regulatory frameworks to address the data challenge (3).

III.A.1. Data collection: unrepresentative data and inequitable predictions
One of the main challenges for a 'fair AI' is to bring more transparency on the collection of data on which data scientists will build and train medical algorithms. ML algorithms learn from the collected data and the higher the quantity of data, the more accurate will be the predictions. Moreover, equitable algorithmic outputs require representative and inclusive data input. At least, two inter-related issues on data collection need to be addressed in this regard: the fact that certain data are missing on the one hand (a), and the importation and/or use of inadequate data on the other hand (b). the use and development of predictive algorithms in medicine, it is crucial to have inclusive data in order to allow ML systems to learn and provide the same quality of prediction for all patients. 78 Therefore, inclusive data for clinical transplantation imply to incorporate such relevant data enabling predictive outputs of equivalent quality for different population groups. However, such data are sometimes lacking, or in a reduced quantity in comparison with the majority population. Lacking data can result from factual considerations and the complexity of having enough representing data due to a limited amount of certain minority populations in a given context. This risk is the 'sample size disparity'. 79 This disparity can also result from structural and/or social factors disadvantaging certain groups including indigenous peoples or women. To give a relevant example on the former in the European context, international agencies but also political institutions within the Sami society have criticized the absence of statecollected Sami population data, leading to a very limited knowledge of the Sami health situation. 80 Such structural issues on health data also concern women. In her influential book Invisible Women, Caroline Criado Perez explains how women were historically excluded from medical research, resulting in data gaps. 81 In particular, clinical trials data on pregnant women are very rare. 82 Even when women were not excluded from research, their specificities were not always considered or deemed relevant. Although there are gender differences in mechanical workings of the heart, lung capacity, immune systems, and sex-based differences in physiology, these differences were broadly overlooked. 83 According to the CESCR in its general comment on the right to health, 'as women are underrepresented in scientific research, it is very common that scientific research and new technologies are gender biased and not sensitive to the particularities and needs of women', 84

• Allocating organs through algorithms and equitable access to transplantation
policies, ie an approach that 'recognizes that biological and socio-cultural factors play a significant role in influencing the health of men and women'. 85 Besides, an imbalance of data in a specific procedure such as organ transplantation may result from various and intertwining factors of disparities in access to waiting lists and transplantation. Addressing the lack of data requires looking upstream for the above-mentioned determinants of health and investigate, inter alia, perceptions of transplantation between different groups of individuals. For instance, the literature has shown that the 'typical recipient is a male', while women are the typical living donors. 86 Disparities may appear early in the process since studies have shown that women and girls access less often to waiting lists and preemptive transplantation then men. 87 Medical factors alone do not explain these differences, and both patient motivation and physicians and parental attitude toward transplantation may contribute to the disparity. 88 Social norms regarding caregiving responsibilities, stereotypes, economic disadvantage, or lack of education and health literacy strongly contribute to the many challenges women with chronic kidney disease face. 89 III.A.1.b. Using inadequate data The use of algorithms in a certain context will only be relevant if data they are fed with represent the targeted patients. Factors such as lifestyle and traditions, beliefs, and medicinal knowledge may affect peoples' health, expression of pain, and likeliness to consult a doctor. 90 Their absence of inclusion in the data may lead to ineffective health predictions for the populations concerned. 91 This issue of the adequate input data may happen at various levels. At the country level for instance, when data emanate from a privileged socio-economic context leading predicting algorithms to disadvantage certain populations. Nicholson Price have shown the 'contextual bias' of algorithms trained in high-resource contexts, and not being adapted to more vulnerable groups. 92 In such circumstances, the algorithms might provide a high quality of care in a given local context by relying on appropriate data in this particular context, but disparities in qualitative predictions may result from the process of translating the algorithms to another socio-economic context 93 or the use of the algorithms at a broader scale. In order to develop effective ML models on organ transplantation and to address data gaps, researchers try to obtain larger amounts of data. Within the frameworks of data protection, one way for respective institutions to achieve this is to share their data in international registries. For example, Eurotransplant and Scandiatransplant have agreements with the ISHLT, the largest repository of heart transplant data. 94 In the absence of such registries or agreements, models may be developed using external data although they are intended for use in other countries/regions. The availability and precision of the data from the United Network on Organ sharing (UNOS) on donor and recipient characteristics and their relationship may explain why it is regularly used outside the USA. 95 The use of external data may raise two issues in relation to equitable access to transplantation: the adaptability of US data to other social and health contexts and where populations may differ significantly, and behind this, the availability, accuracy, and transparency of local data. 96 These risks may be disproportionally affecting certain minorities, such as indigenous populations or immigrants. When Canadian teams feed and train AI models of transplantation on the basis of UNOS database, the health predictions for indigenous communities of Canada for example may be of a significantly lesser quality than for the majority of the population.
Lack of data and use of inadequate data is likely to lead to inequitable health predictions. If such predictions disadvantage certain groups, they may constitute an indirect discrimination on the ground(s) of gender, national minorities, or 'other status', including socio-economic status. It is arguable that within a country, the circumstances of certain groups are 'relevantly and significantly' different on health matters, requiring state authorities to take positive measures to make individuals' right to be free from discrimination as such or in conjunction with their right to health (or right to life) effective.

III.A.2. Data pre-processing: the case of variable selection for organ transplantation
Another threat to equitable access to organ transplantation might come from the preprocessing of the data. Pre-processing includes data cleaning, data transformation and 93  data reduction. Data cleaning is the handling of missing or noisy data. Data transformation encompasses various tasks including the selection of features in the data that will constitute the variables of the algorithms. Depending on the objective of the algorithms, data scientists will select variables that they believe that are relevant to answer a specific question. This characterization of input data might be biased and lead to potentially inequitable outputs or be constitutive of a direct discrimination based on a protected characteristic. The use of 'race' as a relevant category in medicine is a striking example of that risk. The absence of intent to discriminate is irrelevant. First of all, it seems important to make a distinction between disaggregation of data based on protected characteristics in order to evaluate the efficiency of health policies on disadvantaged groups and the use of the same category as relevant variable for clinical purposes. While the first is heavily encouraged by human rights bodies in order to achieve effective rights, including in healthcare, 97 the second will be regarded as suspicious. In Europe, the use of ethnic-racial data or on disability served eugenic purposes and the ideology of inferiority of certain populations. The program T4, which led doctors to renounce to the Hippocratic Oath to exterminate disabled persons explains why the use of such data is extremely sensitive and suspicious. The EU General Data Protection Regulation (GDPR) prohibits the collection of this 'sensitive' data, with several exceptions including in medicine and public health. 98 Several countries prohibit the collection of such data and predictive algorithms on transplantation do not refer to race as a relevant criterion. 99 In the United States, in contrast, data are not only disaggregated on the basis of multiple protected characteristics including race/ethnicity in order to improve public health and potentially detect health discrimination. 'Race' is recurrently selected as a relevant variable in the design of predictive algorithms evaluating the risks of chances of a successful transplantation. 100  selection of this variable can be questioned as such, 'race' being a social construct. 101 Researches on human genome have challenged the classification of the population into five races and demonstrated that a small minority of alleles were specific to one geographical region. 102 Thus, the use of 'race' as a variable might be based on the erroneous making of a causal link between certain medical factors and 'race' leading to generalization/overinclusion and inaccuracy. For example, the association between the creatinine serum rate and Black 'race' because of the muscularity of back people has been extremely debated. 103 Moreover, the social nature of this factor explains why self-identification of patients to a certain 'race' might result in poor utility for clinical purposes, although these data can have another social usefulness. 104 From a non-discrimination perspective, algorithmic predictions and classifications on this basis could be analyzed as potential direct discrimination on a racial ground or in association with a national minority (unless proven to be justified-according to the ECtHR's jurisprudence). Besides, in the medical area where variables are not only socially useful, ML generalizations and predictions made on the basis of an association to a protected ground according to self-identification raises the following issue: in order to be protected from direct discrimination on the ground or race/ethnicity, does the person have to effectively belong to the protected group? Or is it sufficient that the decision is based on an assumption? Although this section focus is on the impact of developers' data pre-processing, the risk of algorithmic discrimination based on-possibly inaccurate-perceptions or assumptions goes beyond the issue of self-determination and applies when algorithms erroneously learn by themselves individuals' belonging to protected groups. Such risks are not clearly regulated in Europe. As previously stated, discrimination by association to a protected ground has been applied only when another person, herself part of a protected group, was involved. Discrimination by perception or assumption has only been recognized so far in case of indirect discrimination by the CJEU. 105 When 'race' is selected as a variable, however, and disadvantaging one particular group, direct discrimination is more likely to apply. Nevertheless, despite the openness of the European Commission to consider such form of discrimination as a direct discrimination, 106 Courts have not interpreted direct discrimination as encapsulating discrimination by wrong perceptions or assumptions. Thus, the protection of individuals in these circumstances by non-discrimination law remains unclear.
In addition to being suspicious and biologically irrelevant, the selection of race as a variable for organ transplantation can lead to inequitable outcomes for patients, especially some of the most vulnerable ones. Recent developments in kidney allocation have questioned the use of donor's 'race' as a relevant criterion. Traditionally, the donor variable ' African American' has been considered a risk factor in kidney donation, which led the Kidney Donor Risk Index (KDRI) to place a higher penalty to kidneys from African-American deceased donors. For human transplantation, authors have shown that the substitution of race by APOL1 genotyping in the Kidney Donor Profile Index (KDPI)-based allocation led to more objective and better predictions. 107 More precisely, the substitution has shown that long-term outcomes of kidney transplantation from African American donors with no or one APOL1 RRv were equivalent as European American deceased donors, questioning the relevance of a donor's race as a relevant variable in risk prediction algorithms. In short, while the race factor conducted to exclude some organs, the genotype factor allows to offer more organs for transplantation and/or with a better utility. Providing that kidneys from deceased African-American donors are more likely to be transplanted to African-American patients because of medical factors (HLA, blood type), this variable would better serve the needs of a vulnerable group in access to organ transplantation: African-American patients. 108 Although arguably more accurate, the substitution of 'race' by APOL1 should not lead to disregard other relevant factors of a successful transplant, including the socio-determinants of health. 109 On another hand, the selection of this variable raises important ethical and legal issues related to the consent of living donors to retrieve genetic data information. 110 In this regard, a recent study suggests the interest of African-American adults in genetic testing for living kidney donation. 111 In Europe, the Oviedo Convention regulates the recourse to predictive genetic tests and prohibits 'any form of discrimination against a person on grounds of his or her genetic heritage'. 112 The substitution of 'racial' data by genetic data should not lead to another type of discrimination.

III.A.3. Conclusive observations on addressing the data input challenge
Addressing the inequitable outputs due to lack of and/or use of inadequate data would entail the adoption of relevant domestic and transnational frameworks to collect and use specific and appropriate data as encouraged by the CESCR 113 and to prevent private actors to propagate the biases resulting from the data. Among the ongoing initiatives of the EU to strengthen the internal market for data, the proposal for a Regulation on the European Health Data Space may have the potential to facilitate the exchange of data between EU countries and to remedy the lack of data and its consequences on equitable access in Europe. 114 In parallel, the EU Proposal of AI Act should contribute to prevent these risks, including for people in situation of socioeconomic vulnerability. 115 The Proposal's current Article 10 imposes appropriate data governance and management practices in, i.e. data collection processes, data preparation, and formulation of assumptions which would apply to the above-mentioned issue of feature selection. 116 The processing of sensitive data is submitted to strict conditions and necessitates appropriate safeguards to protect fundamental rights. 117 The Act also 110 See GDPR, which affords special protection against the processing of special categories of data, including on racial or ethnic origin, genetic data and data concerning health: art. 9 (h) and (i), 3 and 4. On genetic testing and human rights in Europe see the Additional Protocol to the Convention on Human Rights and Biomedicine, concerning Genetic Testing for Health Purposes, Strasbourg, Nov. 27, 2008 ETS 203, entered into force on July 1, 2018, but has very few ratifications so far. Providing that most kidneys come from deceased donors and are most likely allocated through an equitable system, one could ask if the consent to the retrieval of genetic information on a deceased body can be implied from that person's (explicit) consent to organ donation; or if a proper consent would be needed. Although these issues will affect the availability of organs and therefore, indirectly in a context of scarcity, the equitable access to organs, it does not properly relate to the use of algorithms and falls beyond the scope of this paper. . These are market-based instruments. 115 Proposal of Artificial Intelligence Act, art. 10. But art. 10 does not refer to discrimination or equity, rather using the more technical notion of bias. Compromise texts do not change this. The first compromise text (Presidency compromise text, Nov. 29, 2021) added to the list of prohibited practices the placing on the market, putting into service or use of AI system that exploits any of the vulnerabilities . . . due to 'social or economic situation . . . ' (art. 5-1(b)). 116 For high-risk AI systems, Proposal of Artificial Intelligence Act, art. 10 (2). See also Convention 108+, inspiring from national legislations on data protection to impose the quality of data (art. 5). 117 Proposal of Artificial Intelligence Act, art. 10(5).
imposes the relevance and representativeness of data sets, although it does not define these concepts, and requires a continuous monitoring. When it comes to the risk of data inadequacy, '[t]raining, validation and testing data sets shall take into account, to the extent required by the intended purpose, the characteristics or elements that are particular to the specific geographical, behavioral or functional setting within which the high-risk AI system is intended to be used'. 118 Importantly, while the core provisions of the original draft did not include non-discrimination, the compromise text amended Article 10 by requiring appropriate governance in view of possible biases 'that are likely to [ . . . ] lead to discrimination prohibited by Union law'. 119 The draft regulation does not, however, integrate a transversal and interdependent approach to fundamental rights. For example, data collection should be culturally appropriate and promote the participation of targeted populations. Inclusion is a necessary step to achieve equity in access to health resources.

III.B. Algorithmic Architecture and the Risk to Exacerbate Vulnerability
The design of the algorithms on organ transplantation can also threaten equitable access to organs. This concerns both non-learning algorithms for organ allocation and ML algorithms. In the first case, risks may arise when developers-the 'architects' of organ allocation algorithms, need to define the algorithmic 'target', 120 ie determine who is the good candidate for transplant, and choose a relevant type of algorithm. 121 The development of the organ allocation algorithm embodies, crystallizes, and extends the public health debates related to the principles of allocation of scarce health resources, and the balance between utility and equality. Although state authorities have discretion in this divisive issue of scarce resource allocation, human rights law imposes a transparent process to achieve equitable access, 122 with due account for collective interests and individual rights and an immediate obligation not to discriminate.
Members of the transplant community have been using algorithms for matching donors and recipients at a national or international level for decades. When defining what makes a good transplant candidate, the algorithm 'architect' has constraints and can make decisions that may impact equitable access to organs. The following sections discuss the determination and incorporation of criteria for organ allocation into the algorithms, whether established by the government or specific policies (thus appearing as normative constraints) or, in the absence of such criteria, designed by developers themselves. While the first subsection deals with allocation of organs from 118 Id. 10 (4). 119 Id. at 3. See also art. 3(44)(c) of the Compromise text, adding 'breach of obligations under Union law intended to protect fundamental rights' in the definition of 'serious incidents', see also art 9(2)(a). The text will likely evolve but it will be interesting to follow up on the broader consideration to human rights in the definitive text. 120 Barocas  deceased donors, traditionally based on the principle of distributive justice (1), the second section investigates the matching of living kidney donors and recipients in kidney exchange programs (2). Both subsections show the risks of imbalance between utility and equality, or even discrimination, in algorithmic design. The third subsection provides concluding observations on addressing these multiple risks (3).
III.B.1. Allocation of deceased donors' organs: utility over equality in algorithmic organ allocation? Due to cold ischemia, the time factor is important in organ procurement from deceased donors. Algorithms help to cope with this medical constraint by integrating and crossing a multiplicity of factors allowing to identify the best potential recipients in a short time. In order to determine who will be the best candidate(s) for transplant of a specific organ, algorithms rely on criteria of medical compatibility and point score systems, attributing different amounts of points to various features identified by policy guidelines or other medical factors. In many cases indeed, the selection of relevant features defining a good or best transplant candidate is an application of public health and ethical norms, guidelines, or standards on fair allocation of scarce health resources. However, data scientists have room to determine allocation rules. This section elaborates on some early stages of algorithmic planning and development, showing that potential threats to equitable access to organs-such as defined in Section II-may find their origin in the very definition of the objective of the algorithm and the following algorithmic classifications. 123 Most transplanted organs come from deceased donors. Organ recovery from a deceased person can only take place if consent or authorization by the deceased and/or her relatives has been retrieved. 124 It is up to the authorities to determine how to allocate the harvested organs according to public health needs. Indeed, unlike living organ donation, which is most of the time directed to a specific recipient, 125 deceased organ donations are mainly allocated according to collective values, starting with the principle of justice and following national or international waiting lists when countries collaborate. In the Eurotransplant system for instance, the ETKAS program (Eurotransplant Kidney Allocation System) for donors below 65 years old integrates different allocation factors, including collective values and interests. First, it relies on medical factors: the donor and recipient characteristics [HLA, Mismatch Probability (MMP), high urgency, kidney after liver transplant]. 126 Second, the algorithm also incorporates the principle of justice and equitable access between individuals and regions or countries by relying on the time spent on the waiting list and by the incorporation of a point system establishing a balance between member countries and regions. Third, Eurotransplant takes into account the vulnerability of certain patients 123 In their referential paper on algorithmic biases, going beyond the data challenge, Barocas  by taking positive action by awarding two types of bonus to paediatric patients. 127 Fourth, the algorithm considers the geographical distance between the donor center and the transplant center, which reflects utility and pragmatism (due to ischemia). Fifth and finally, it is worth observing that except Germany, other member countries of Eurotransplant give a bonus for patients having donated one of their kidneys before. 128 The integration of this moral/social worth feature reflects the choice to valorize an original altruistic action by applying reciprocity. Some defend even further social worth or individual responsibility as relevant criteria to determine who can get an organ. 129 The collective values and interests defining prioritization in access to organs differ depending on the regions and the period. They are heavily impacted by political choices. In the early stages of dialysis in the United States for instance, doctors decided that only patients living in Washington would have access to this technology because the State of Washington had financed its development. 130 The different levels of investment (in research, infrastructures, etc.) in transplantation still contribute to explain today the disparities between regions in transplantation rates. These disparities are also related to different conceptions of equitable access and the level at which it should be achieved when increasing numbers of people on waiting lists foster competition for self-sufficiency in organs between regions on a national scene, and countries on the international scene. 131 Even when prioritization is not based on moral virtue but on factors of medical relevance of apparent neutrality, such policies can infringe equitable access to organ transplants by having negative impacts on certain groups. Efficiency-based algorithms may result in an aggravation of the state of vulnerability of certain patients. For example, it has been shown in the United States that a purely efficiency-based algorithm targeting the greatest increase in quality-adjusted life expectancy would result in the assignment of lower priorities to African-American patients because of their higher risk of graft failure. 132 While relying on an apparently neutral target, the algorithm disfavored a group of people protected by the law. Besides, in 2011, the OPTN proposed a policy to establish a kidney profile index (KDPI) and to reserve the best 20 per cent of kidneys to the 20 per cent of the candidates with the highest estimated post-transplant survival (EPTS) and to allocate the remaining 80 per cent of kidneys by age-matching whereby candidates within 15 years of the donor are given priority. 133 On top of the risk to indirectly discriminate certain groups of patients, the criterion of longevity for organ allocation strongly correlates with age and may lead ML algorithms to systematically exclude the elderly from eligible transplantable candidates to the extent that it could be analyzed in terms of direct discrimination. Unlike the Supreme Court of the United States, which does not consider age as a suspect class under the equal protection clause, 134 European Human rights law recognizes the elderly as protected category. The EU Charter of fundamental rights proclaims the equality before the law 135 and includes 'age' in its non-limitative list of protected groups against discrimination. 136 At the Council of Europe level, the Committee of Ministers of the Council of Europe adopted a recommendation in 2014 reminding that '[o]lder persons shall enjoy their rights and freedoms without discrimination on any grounds, including age and recommending Member States to make explicit reference to "age" in their national anti-discrimination legislation'. 137 In addition, the issue of equitable access to healthcare for the elderly is among the planned actions of the strategic plan for 2020-2025. 138 Beyond the special case of transplantation, the elderly lack of information and suffer from 'agebased rationing, priority-setting, and poorly founded concerns regarding their capacity to make healthcare decisions'. 139 In a context of scarcity, the optimization of organ allocation is a legitimate purpose that will meet without any doubt the first part of the test under non-discrimination law. There are different approaches to the consideration of age in the allocation of health care resources. 140 However, one can wonder if the mathematical assessment and balance of years of life is more acceptable in a distributive justice system than judgments on individuals' social worth. Against the trend to prioritize young patients under 'fair innings' or 'numerical minority' arguments, some authors have argued that such an approach could ignore other determinants of medical wellbeing and undermine equality between patients, by failing to individualize them. 141 The treatment of the elderly is a pressing issue, since one of the challenges for health law in Europe is population aging and 'responding to the ageing of the population is a policy priority for the EU'. 142 At first glance, the level of correlation/causality between the target and age might be so tight that it could meet two of the requirements of direct discrimination: (i) a differential treatment in comparison with other patients and (ii) based on age, a protected ground under European law. However, a central issue here would be the test of comparability of situations, bearing in mind Courts' discretion in this regard (supra). In the terms of the ECtHR, the difference of treatment should be between 'persons in an analogous or relevantly similar situation' in light of the subject-matter and purpose of the contested measure. 143 On the one hand, if Courts were to find that the situations of older patients were relevantly similar to younger patients, then it would be harder for the authorities to prove that the contested measure was justified by external reasons to non-discrimination. 144 On the other hand, if judges were to consider that the level of correlation between the protected characteristicage-was not sufficiently narrow-because it affects other groups for instance, the situation could fall into indirect discrimination due the failure to take specific measures aiming at making effective the rights of members of a protected group. There would then be an application of apparently neutral measures with 'disproportionately prejudicial effects' 145 on individuals belonging to this group. As stated in Section II though, when it comes to health, there are multiple vulnerable groups and States are granted discretion in the management of scarce resources. Moreover, the elderly are a protected group but, as noted above, Courts do not apply the same rigorous standard of review than for other groups such as ethnic minorities. The scrutiny would certainly be on necessity and proportionality. The ECtHR would likely analyze whether the authorities had managed a fair balance between the rights of an elderly applicant and other interests including the utility imperative. In the US example, old people are excluded from the potential recipients of the best kidneys but will continue to be eligible for matching with other kidneys. In Europe, the existence of alternate programs for older recipients such as the specific Senior Program in the Eurotransplant model 146 would likely exclude a finding of disproportion. In that respect, the disadvantage in qualitative transplantation does not appear disproportionate.
When combined with misconceptions or discriminatory assumptions on the good quality of life, the logic of utility sometimes leads to deny individuals with physical or mental disabilities access to the waiting list and to organ transplants. 147 In the USA, discrimination in access to waiting list and transplant led the federal government to adopt the Charlotte Woodward Organ Transplant Discrimination Prevention Act in 2019. 148 Although the above still holds true for persons with disabilities, they belong to a protected class to which international and European human rights law offer a higher protection. Therefore, a scrutiny of algorithmic outputs based on longevity matching would be stricter than for old people if they were prejudicial to persons with disabilities.
Finally, one last issue concerns the extent of positive measures at the algorithmic level to address inequities. Relying on socio-economic determinants of health, is there a right to algorithmic compensation for disparities in physical accessibility to transplantation that would imply to give more weight in algorithms in the same way as for medical necessity? Physical accessibility is directly linked to the rules of transplant allocation. As previously stated, available organs mainly come from deceased donors. For reasons related primarily to ischaemia, countries have adopted organ allocation policies that favor local allocation. Therefore, depending on respective hospitals' transplantation activity, there is a correlation between regional (post-mortem) donation rates and regional transplant rates. 149 Providing that there is often a correlation between geographical disparities and economic disparities and that discrimination on the basis of one's economic situation is prohibited, States have a strong interest in taking positive measures to address inequities by adapting their allocation policies.

III.B.2. Matching living donors and recipients in kidney exchange programs: identifying the good match and the interplay with donor and physician subjectivity
The development of living donor kidney exchange programs may also, somewhat unexpectedly, raise some issues of equitable access. Living donor kidney transplantation is the preferred mode of treatment for patients suffering from end-stage kidney disease, since it allows for longer graft survival than transplantation of a deceased donor kidney. 150 In most cases, living donations of kidneys do not involve the development of allocation algorithms since a person consent to give a kidney to a specific person she is usually bound with and her kidney is not put in a pool. However, following the examples of Switzerland and the Netherlands, several European countries have developed crossed or paired kidney donations between living donors and potential recipients. 151 152 The number of pairs involved varies from jurisdiction to jurisdiction, such as the inclusion of non-directed donors in the chain (when such 'altruistic' donations are allowed). When several pairs are involved, countries may cooperate in different forms to find better matches. Scandiatransplant for instance has developed a transnational kidney exchange program between its members by merging the pools of pairs. 153 Since the allocation of kidneys in the paired-donations programs is supposed to be based on exchange and reciprocity, the issue of equitable access to organs should not be relevant here, at least not directly. Yet, the management of paired-donations and the design of matching models-especially at a broader scale-may have an effect on equitable access to organs that I discuss below.
As with the allocation of organs from deceased donors, mathematical models are used to match donors and recipients, based on a variety of criteria including blood type, HLA data, clinical information, etc. 154 One thing to consider is that unlike deceased donations though, which rely, most of the time, 155 on the principle of distributive justice, living donations are mostly directed to a specific recipient. 156 From the donor's perspective, paired donations do not affect the fact that the consent to give a kidney is intrinsically linked to the therapeutic need of a designated recipient. Hence, one pertinent issue from an ethical and cultural perspective might be to determine if living donors in such chains have a right or interest to direct the kidney donation according to subjective criteria, thus impacting the allocation criteria of kidneys in those chains. partially replies (1). Furthermore, ML may also favor the creation of algorithmic groups unprotected by European standards. States will need to determine whether correlations leading to these new groups are acceptable or not (2).

III.C.1. Socially unacceptable correlations and proxy discrimination
Discrimination or aggravation of vulnerability may result from the algorithmic planning and development, whatever the type of algorithm. When ML is used in particular, the learning process may generate biased outputs that this section focuses on. ML is used for organ allocation strategies but also for clinical prediction and decision support, 170 prediction of waiting list mortality, prediction of graft survival or rejection, to analyze transplant biopsies, etc. ML models may analyze complex heterogeneous datasets and better handle multidimensional interactions between predictors. 171 In contrast with classical programming, ML systems-including DL-are capable of generating rules that can later be applied to other datasets. Therefore, an inaccurate feature selection during data pre-processing such as described supra will likely generate [clinically] inaccurate rules. The irrelevance of retrospective/historical datasets in present-day clinical practice 172 or the imbalance of the input data will affect the model performance to the detriment (for the latter) of underrepresented groups with rarer conditions. However, ML algorithms may also produce discriminatory outputs in the absence of biases or inaccuracy in the data pre-processing. This is usually referred to as unequal ground truth. In ML, ground truth is considered to be 'the best available approximation of reality, expressed in data'. 173 Indeed, supervised ML algorithms establish correlations in large amounts of data, on the basis of which classifications or predictions of numeric outcomes are made. Providing that even strong, correlations are not causation, an inherent and structural issue of ML in regard to non-discrimination is the inevitable generalization and the lack of individualization. The risk of inaccurate association with a certain group exposes patients to discrimination by perception, or even multiple or intersectional discrimination that current European law cannot capture. 174 Although human reasoning might fail to offer better solutions and ML improves patients' health precisely because of these correlations with a high quantity of data, this issue is of particular concern due to the complexity and opacity of algorithmic systems.
Such correlations are even more suspicious when ML systems detect 'proxies'. 175 It is the case when, despite the absence of selection of protected characteristics as relevant features, the algorithm relies on variables correlated with membership in a protected group. Researchers frame this problem as 'redundant encodings'. 176 Blinding the algorithm to sensitive features may even aggravate the negative effects since the detection of discrimination becomes harder. 177 A relevant example is the processing of residency data by predictive models, when it correlates to origin and ethnicity. As stated supra, 178 locality is taken into account in deceased donors' organ allocation algorithms because of the few hours of preservation of organs after their recovery. The development of ML based on such data will lead algorithms to make correlations on the basis of locality. However, this pragmatic criterion might hide, in a significant proportion, membership in protected classes, such as ethnicity, or strongly correlate with other types of vulnerability.
In order to mitigate these risks, computer scientists have developed computational techniques such as discrimination-aware data mining and fairness, accountability, and transparency in ML (FATML). Such techniques enable them to evaluate the differences in outcomes and measure equity and provide 'reasonable ways to measure fairness'. 179 They have inspired from non-discrimination law and privacy, including the GDPR. 180 Nonetheless, there is a trade-off between utility and bias minimization in the algorithmic development, 181 and the attempt to minimize socially unacceptable (yet exact) proxy discrimination will affect the algorithms' predictive accuracy. 182 European non-discrimination law only partially captures the risk of algorithmic discrimination, and in particular proxy discrimination. First, proxy discrimination will rarely enter within the scope of direct discrimination. The CJEU considers multiple factors in identifying discrimination on the basis of ethnicity, but these must be causally related to the protected characteristic in order to identify a direct discrimination. 183 In case of proxy discrimination, the degree of overlap between a given proxy such as residency and the protected ground could be insufficient to establish the narrow link that the Court of justice requires for direct discrimination. 184 Second, European nondiscrimination law is currently unable to address in itself multiple and intersectional forms of vulnerabilities in health and transplantation. Such intersectional forms preexist in society and show up in the data, and the inability to address the complexity of their interactions would require a preventive approach.

III.C.2. Potentially unacceptable correlations: harms to non-legally
protected algorithmic groups Alongside the risk of making socially unacceptable correlations, ML may also identify new groups and generate new types of algorithmic differentiation that do not fit either the protected characteristics or the logic of non-discrimination law. For example, Dutch insurance companies have been shown to charge extra for car insurance when customers lived in apartments which numbering including a letter. 185 Other groups such as 'dog owners', 'single parents' have been created by AI systems for purposes of advertising and personalized pricing. 186 The use of ML and the diversification of the sources and nature of data, which also applies to health and transplantation, will call for an assessment, within comprehensible distinctions, 187 between acceptable and unacceptable differentiations from the perspective of equitable access to transplantation. In this assessment, one challenging aspect is that new algorithmic groups are instable 188 and deprived of social meaning. 189 If we consider that such new groups may be the subject of unfair treatment, there are several obstacles to address this issue under the framework of this article. First, such grounds are unlikely to be interpreted, as such, as new protected grounds. Although protected grounds are most of the time non-exhaustive under non-discrimination law, 190 leaving room to consider new and concomitant forms of vulnerabilities, their particular selection by the drafters of the treaties was a response to social or historical prejudices and oppressions. 191 Similarly, European Courts have protected new groups of persons, on the same rationale. Hence, it would be surprising if Courts were to recognize algorithmic grounds as protected 'other status' or 'such as' groups historically or socially stigmatized. Second, assuming a contrario that some algorithmic groups were to be recognized as new protected grounds due to algorithmic stigmatization and prejudicial effects (under the category of indirect discrimination), the concrete analysis of discrimination, in particular the test of comparability between situations, might be complex in view of the intersecting characteristics composing the algorithmic groups. Third, for the above-mentioned reasons, addressing algorithmic stigmatization based on non-protected grounds would require to understand equitable access beyond the narrow borders of non-discrimination law and interpreting the principle of equality in the algorithmic context. However, as discussed in this paper, European human rights law recognizes that States are in a better position to assess and achieve substantive equality in the allocation of health resources, beyond their immediate obligation not need to be constantly revaluated in order not to result in new inequalities, and a fair balance should be reached in addressing different sources of vulnerability. Besides, and as already mentioned, redistribution resulting in algorithmic compensation needs to account for studies on social determinants explaining disparities in access in a broader context. 205 Regarding the use of the planning and development of ML, States should facilitate the collection and use of appropriate data by adapting their legislation to achieve equivalent quality predictions between patients. The EU AI Act will partly contribute to the achievement of this mission since it will be directly applicable in the EU Member States. Since developing ML with new datasets is costly, there is a compelling interest in ensuring the inclusiveness and appropriateness of data itself and collection methods in the first place. In the meantime, mitigating biases can limit disadvantage on certain groups.
Third, States need to foster education on ethics and human rights for data scientists as well as in medical schools. 206 Interdisciplinary dialogue must be strengthened in order to effectively prevent the risk of potentially discriminatory algorithmic outputs.

FUNDING
This research is part of the first UCPH Data+ project co-funded by the Faculty of Law and the Department of Computer Science DIKU at Copenhagen University.